Abstract
Mental health problems are prevalent among the working population and must be resolved. We conducted a web-based large-scale study of workers, including a baseline survey and two follow-up surveys, to investigate the associations between self-care behaviors in daily life (e.g., stress coping, sleep, diet, and exercise), work environment improvements, and mental health among Japanese employees from various industries. In this protocol, we demonstrate the study design and demographic data of the participants in the baseline survey. Invitations to participate in this study were sent to 421,825 internet monitor registrants in February 2022. Overall, 20,000 registrants who met the inclusion criteria participated in the survey. There were large variations in occupations and working styles (e.g., physical work, night work, and teleworking) among the participants, and we also found significant differences between male and female participants in the demographic data. An overview of the survey data suggests that the demographic characteristics of the participants in this study are comparable to those reported in previous studies on Japanese employees. We plan to use these survey data in the future to examine the associations of daily lifestyles and work environments with the mental health of Japanese employees.
Keywords: Mental health, Work environment, Stress, Cohort, Self-care behavior
Introduction
Mental health problems are prevalent in the working population. More than 50% of employees in Japan have experienced anxiety, worry, and distress in their occupational life, and in 10% of companies in Japan, employees have taken a leave of absence and/or left their jobs due to mental health problems in the previous year1). In December 2015, the Japanese government launched a new occupational health policy called the Stress Check Program, which aimed at preventing mental health problems in the workplace2). The program requires employers to conduct an annual survey of the workers’ psychological stress and arrange for an interview with a physician if a worker with a high stress level requests for one. In addition, employers are required to analyze data from stress checks in relevant groups (e.g., departments and units) and use the results to improve the psychosocial work environment. Furthermore, employers are encouraged to provide information on stress management (i.e., self-care behaviors for stress in daily life) when workers receive the results of stress checks. In this program, improving the work environment and promoting self-care behaviors for stress are considered as important factors in the primary prevention of mental health problems.
However, there is no robust scientific evidence on the effects of work environment improvements and self-care behaviors on mental health. The effects of work environment improvement on mental health have been previously investigated in Japan in a sample of occupations (e.g., manufacturing workers3) and nurses4)). However, these studies have been conducted on workers from one company in a specific industry, and the overall picture of the effects of work environment improvement is less clear because variations in industrial types, occupations, and working styles could largely alter work environment improvement and their effects on mental health. It has been reported that providing information on self-care, such as stress coping, sleep, diet, and exercise, is not always effective in improving mental health5), and the relationship between individual self-care behaviors and mental health is often unknown.
To make these scientific evidences more robust and general, we have planned and conducted a web-based large-scale cohort study called the “Web-based Longitudinal study of the Work Environment and daily Lifestyle (WELWEL)” that investigated the longitudinal associations between self-care behaviors, work environment improvements, and mental health outcomes among Japanese employees, including those from various industries and occupations. A portion of the survey data has already been analyzed and presented6). This paper aims to describe the study design and demographic data of the participants in the baseline survey.
Subjects and Methods
Survey and sampling
WELWEL is an ongoing large-scale study of workers, including a baseline survey (February 2022) and two follow-up surveys at intervals of one year (February 2023 and February 2024). Data collection was performed online on internet monitors, which were outsourced to a research company called Hamon Corporation.
In the baseline survey, Japanese workers who had registered with internet monitors were sampled according to the distribution ratios of all Japanese employees7) for 20 industry types (e.g., construction and manufacturing), age groups (20–29, 30–39, 40–49, and 50–59 yr), and sex. We further set the exclusion criteria for the sample as follows: (a) agriculture, forestry, fishery, mining, and quarry workers; (b) those who were self-employed or company executives; (c) those who had more than one job; (d) those who worked <20 h per week; and (e) those who responded inappropriately to the instructional manipulation check (details presented in a latter section) and/or had an extremely short response time (≤10 min). This study targeted employees, and workers falling under criteria (b), (c), and (d) were excluded because their working environment was expected to differ significantly from that of general employees. Furthermore, the percentage of employees in primary industries (e.g., agriculture and forestry) was considerably lower than that in other industries in Japan, and we excluded workers in those industries. Invitations to participate were sent to 421,825 registrants, 115,094 of whom accessed the company’s survey website. Registrants who responded inappropriately to the instructional manipulation check and/or had extremely short response times (N=4,738) were excluded; the first 20,000 registrants who met the distribution ratios for Japanese employees and did not meet the exclusion criteria participated in the survey.
Informed consent was obtained from all participants, and the study was approved by the Research Ethics Committee of the National Institute of Occupational Safety and Health, Japan (2021N-1-19).
Outcomes
The mental health outcomes included mental health status, presenteeism, sick leave, and the use of antidepressants. The mental health status was assessed using the Japanese version of the K6 scale8, 9). The K6 scale has six items that ask about frequently experienced symptoms of psychological distress (e.g., feeling so sad that nothing can cheer you up) over the past 30 days. Response options range from 0 (none of the time) to 4 (all the time). The reliability and validity of the K6 scale were evaluated using a Japanese sample. Poor mental health was considered present when participants showed K6 scores of ≥13, indicating severe mental illness10).
Presenteeism, a decline in workers’ work performance due to illness or other medical conditions, was assessed using one item from the Japanese version of the World Health Organization Health and Work Performance Questionnaire (HPQ) short form11). Participants were asked to rank their work performance over the past four weeks (28 d) from 0 to 10, with 0 indicating the worst work performance and 10 indicating the best work performance. The presenteeism score was calculated as a scale score × 10 (range: 0–100), with higher scores indicating no lack of performance. The cutoff score for being frequently absent from work because of mental health problems is 40, with scores below 40 indicating greater presenteeism12).
Sick leave was assessed using a single question (How long have you been absent from work because of sickness or poor health during the past year?), and four response options were provided (not at all, 1–6 d, 1 wk to 1 month, and >1 month). Participants who chose “1 wk to 1 month” or “over 1 month” were further asked to choose causes of sickness or poor health from 11 disease options (cancer, mental health problems, respiratory disease, liver disease, stroke, heart disease, kidney disease, diabetes mellitus, hypertension, gynecological disease, and the other diseases). An additional question was asked about turnover due to sickness or poor health in the previous year because non-regular workers in Japan might not be covered by the sick leave system and were more likely to leave their jobs than be absent from work due to sickness13). Participants who reported experiencing turnover were further asked to choose the causes of sickness or poor health from 11 disease options.
The history of antidepressant use in the previous year was determined using a single question by providing a list of antidepressants widely available in Japan.
Demographic and occupational characteristics
Participants were asked about their basic demographic characteristics (e.g., age, sex, residential area, marital status, education, and income) and occupational characteristics (e.g., occupation, tenure in the company, company size, working hours, employment status, physical work, night shift, telework, and participation in the stress check program in the previous year).
The other items
The survey included items about self-care behaviors related to stress in daily life and work environment improvements. We briefly describe the overviews of these items because, in this protocol, we did not demonstrate data on self-care behaviors and work environment improvement.
This study investigated self-care behaviors in daily life, which could be beneficial for reducing stress and preventing mental health problems. Briefly, stress management behaviors, conversation time in daily life, activities during off-job time, behavioral intentions, and actual behaviors for seeking support in the workplace were each assessed using a single question. Furthermore, questions on daily lifestyle relevant to sleeping (e.g., sleeping hours, napping, physical conditions before and during sleep, and sleep quality), eating (e.g., breakfast eating, dietary balance, meal time, and eating alone or with someone), and exercise (e.g., frequency of exercise and motivation for exercise) were asked.
Participants were asked whether they had experienced work environment improvement activities using 24 items (e.g., staffing levels and workloads were adjusted to avoid giving work to only specific individuals or teams). These items were originally developed in accordance with a previous checklist for work environment improvement14). For each item, 5 options were provided: “Experienced for more than a year”, “Recently experienced in the past year”, “Not experienced but necessary in the future”, “Not experienced and not particularly necessary”, and “Not applicable to my workplace”.
In addition to the experience of work environment improvement, psychosocial factors at work (e.g., job demand, job control, and work engagement) were measured using the Brief Job Stress Questionnaire15) and subscales of the New Brief Job Stress Questionnaire16).
Instructional manipulation check
Considering that certain participants from internet monitors could be insincere when responding to the survey, we set a question for an instructional manipulation check, which was previously utilized17). In this question, five decision branches (ranging from “agree” to “disagree”) were presented; however, in the question, the participants were instructed not to select any branch as part of the survey design to detect participants who provided insincere responses.
Sample size calculation
The primary outcomes of this study were poor mental health (K6 score ≥13) and sick leave for more than one month due to mental health problems (including turnover), as assessed in the follow-up survey. We planned to apply logistic regression analysis for the two outcomes, in which the number of work environment improvements, self-care behaviors (e.g., stress management behaviors, daily lifestyle relevant to sleeping, eating, and exercise), and some confounding factors (e.g., age, sex, income, industry, occupation, company size, working hours, and employment status), assessed at the baseline survey, were included as independent variables. Furthermore, we planned to conduct subgroup analyses of these associations (e.g., secondary vs. tertiary industries).
The required sample size was determined to be 20,000, regarding a previous simulation in logistic analysis18), which indicated that the number of events (incidents) should be at least 10 times the number of independent variables to minimize biased coefficients. The 12-month prevalence of depressive disorder, the most common mental disorder, is reportedly 2.6% among Japanese employees19). In addition, based on our previous experience of conducting an internet survey, we expected that approximately 50% of the participants would drop out during follow-up for a number of reasons (e.g., not being registered as internet monitors at the follow-up survey and lack of time to participate in the follow-up survey)20, 21). Taken together, 200–300 employees (2.6% of the remaining 10,000 participants) would report poor mental health and sick leave during the follow-up survey, allowing up to 20–30 independent variables to be included in the logistic analysis.
Statistical analyses
In this study, we demonstrated data from the baseline survey on the demographic and occupational characteristics of participants, as well as mental health outcomes. Differences in baseline survey data between male and female participants were analyzed using χ2 and independent t-tests. Furthermore, considering possible selection bias, differences in the data between the final sample (N=20,000) and participants excluded by the instructional manipulation check and/or short response times (N=4,738) were analyzed using χ2 tests and independent t-tests.
Results
The demographic and occupational characteristics of the participants were shown in Tables 1 and 2. There were significant differences in demographic and occupational characteristics between male and female participants. For example, male workers were more likely to be smokers (χ2(1)=829.5, p<0.001) and obese (χ2(1)=672.8, p<0.001) than were female workers. Among male participants, more workers were in managerial, technical, and production positions, while among female participants, more workers were in clerical and service occupations (χ2(8)=3667.2, p<0.001). Female participants were more likely to have irregular employment (χ2(1)=2,270.6, p<0.001), work shorter hours (χ2(4)=1,648.5, p<0.001), and earn lower incomes (χ2(2)=515.2, p<0.001) than male participants.
Table 1. Demographic characteristics of male (N=11,011) and female (N=8,989) participants.
| Total | Male | Female | p | ||
|---|---|---|---|---|---|
| Age, means ± SD | 41.7 ± 10.2 | 42.1 ± 10.1 | 41.2 ± 10.3 | <0.001 | |
| Age groups, n (%) | <0.001 | ||||
| 20–29 yr | 3,461 (17.3) | 1,764 (16.0) | 1,697 (18.9) | ||
| 30–39 yr | 5,012 (25.1) | 2,845 (25.8) | 2,167 (24.1) | ||
| 40–49 yr | 6,295 (31.5) | 3,482 (31.6) | 2,813 (31.3) | ||
| 50–59 yr | 5,232 (26.2) | 2,920 (26.5) | 2,312 (25.7) | ||
| Resident area, n (%) | <0.001 | ||||
| Hokkaido region | 856 (4.3) | 432 (3.9) | 424 (4.7) | ||
| Tohoku region | 1,154 (5.8) | 608 (5.5) | 546 (6.1) | ||
| Kanto region | 7,526 (37.6) | 4,485 (40.7) | 3,041 (33.8) | ||
| Chubu region | 3,813 (19.1) | 2,056 (18.7) | 1,757 (19.5) | ||
| Kinki region | 3,583 (17.9) | 1,930 (17.5) | 1,653 (18.4) | ||
| Chugoku-Shikoku region | 1,517 (7.6) | 735 (6.7) | 782 (8.7) | ||
| Kyushu region | 1,551 (7.8) | 765 (6.9) | 786 (8.7) | ||
| Married status (married), n (%) | 10,308 (51.5) | 6,225 (56.5) | 4,083 (45.4) | <0.001 | |
| Education (>12 yr), n (%) | 14,752 (73.8) | 8,218 (74.6) | 6,534 (72.7) | 0.002 | |
| Annual household income, n (%) a) | <0.001 | ||||
| <4 million yen | 5,977 (29.9) | 2,567 (23.3) | 3,410 (37.9) | ||
| 4–8 million yen | 9,125 (45.6) | 5,405 (49.1) | 3,720 (41.4) | ||
| ≥8 million yen | 4,898 (24.5) | 3,039 (27.6) | 1,859 (20.7) | ||
| Smoking status (smoker), n (%) | 4,470 (22.4) | 3,305 (30.0) | 1,165 (13.0) | <0.001 | |
| Obesity (BMI ≥25 kg/m2), n (%) | 3,760 (18.8) | 2,783 (25.3) | 977 (10.9) | <0.001 | |
| History of COVID-19 infection, n (%) | 436 (2.2) | 234 (2.1) | 202 (2.2) | 0.557 | |
a) 1 million yen=USD 7,415 as of 8 May 2023.
SD: standard deviation; BMI: body mass index; COVID-19: coronavirus disease 2019.
Table 2. Occupational characteristics and mental health outcomes of male (N=11,011) and female (N=8,989) participants.
| Total | Male | Female | p | ||
|---|---|---|---|---|---|
| Categories of industry, n (%) | <0.001 | ||||
| Construction | 1,505 (7.5) | 1,259 (11.4) | 246 (2.7) | ||
| Manufacturing | 3,743 (18.7) | 2,618 (23.8) | 1,125 (12.5) | ||
| Electricity, gas, heat supply and water | 124 (0.6) | 101 (0.9) | 23 (0.3) | ||
| Information and communications | 743 (3.7) | 540 (4.9) | 203 (2.3) | ||
| Transport and postal activities | 1,131 (5.7) | 889 (8.1) | 242 (2.7) | ||
| Wholesale and retail trade | 3,315 (16.6) | 1,556 (14.1) | 1,759 (19.6) | ||
| Finance and insurance | 598 (3.0) | 258 (2.3) | 340 (3.8) | ||
| Real estate and goods rental and leasing | 344 (1.7) | 200 (1.8) | 144 (1.6) | ||
| Scientific research, professional and technical services | 711 (3.6) | 443 (4.0) | 268 (1.6) | ||
| Accommodations, eating and drinking services | 1,078 (5.4) | 411 (3.7) | 667 (7.4) | ||
| Living-related and personal services and amusement services | 711 (3.6) | 275 (2.5) | 436 (4.9) | ||
| Education, learning support | 1,060 (5.3) | 430 (3.9) | 630 (7.0) | ||
| Medical, health care and welfare | 2,792 (14.0) | 630 (7.0) | 2,162 (24.1) | ||
| Compound services | 203 (1.0) | 122 (1.1) | 81 (0.9) | ||
| Services, n.e.c. | 1,117 (5.6) | 662 (6.0) | 455 (5.1) | ||
| Government, except elsewhere classified | 825 (4.1) | 617 (5.6) | 208 (2.3) | ||
| Occupation, n (%) | <0.001 | ||||
| Manager | 1,919 (9.6) | 1,745 (15.8) | 174 (1.9) | ||
| Professional | 2,984 (14.9) | 1,540 (14.0) | 1,444 (16.1) | ||
| Technical | 1,357 (6.8) | 1,177 (10.7) | 180 (2.0) | ||
| Clerical | 5,755 (28.8) | 2,054 (18.7) | 3,701 (41.2) | ||
| Production | 2,907 (14.5) | 2,256 (20.5) | 651 (7.2) | ||
| Service | 4,231 (21.2) | 1,718 (15.6) | 2,513 (28.0) | ||
| Others | 847 (4.2) | 521 (4.7) | 326 (3.6) | ||
| Tenure in the company, n (%) | <0.001 | ||||
| <1 yr | 1,701 (8.5) | 801 (7.3) | 900 (10.0) | ||
| 1–3 yr | 9,744 (48.7) | 4,666 (42.4) | 5,078 (56.5) | ||
| ≥3 yr | 8,555 (42.8) | 5,544 (50.3) | 3,011 (33.5) | ||
| Company size, n (%) | <0.001 | ||||
| ≤49 persons | 6,736 (33.7) | 3,043 (27.6) | 3,693 (41.1) | ||
| 50–999 persons | 8,123 (40.6) | 4,601 (41.8) | 3,522 (39.2) | ||
| ≥1,000 persons | 5,141 (25.7) | 3,367 (30.6) | 1,774 (34.5) | ||
| Working hours per week, n (%) | <0.001 | ||||
| 20–30 h | 2,925 (14.6) | 960 (8.7) | 1,965 (21.9) | ||
| 30–40 h | 5,582 (27.9) | 2,449 (22.2) | 3,133 (34.9) | ||
| 40–50 h | 8,636 (43.2) | 5,378 (48.8) | 3,258 (36.2) | ||
| 50–60 h | 1,813 (9.1) | 1,402 (12.7) | 411 (4.6) | ||
| ≥60 h | 1,044 (5.2) | 822 (7.5) | 222 (2.5) | ||
| Irregular employment, n (%) | 4,800 (24.0) | 1,211 (11.0) | 3,589 (39.9) | <0.001 | |
| Physical work, n (%) | <0.001 | ||||
| 0 h | 7,709 (38.5) | 3,913 (35.5) | 3,796 (42.2) | ||
| Less than 1 h | 7,796 (39.0) | 4,323 (39.3) | 3,473 (38.6) | ||
| More than 1 h | 4,495 (22.5) | 2,775 (25.2) | 1,720 (19.1) | ||
| Night work, n (%) | 2,166 (10.8) | 1,457 (13.2) | 709 (7.9) | <0.001 | |
| Telework (≥5 d per week), n (%) | 1,041 (5.2) | 672 (6.1) | 369 (4.1) | <0.001 | |
| K6 score, mean (SD) | 4.8 ± 5.7 | 4.4 ± 5.6 | 5.2 ± 5.8 | <0.001 | |
| Poor mental health (K6 score ≥13), n (%) | 2,271 (11.4) | 1,141 (10.4) | 1,130 (12.6) | <0.001 | |
| Sick leave due to mental health problems, n (%) | 0.386 | ||||
| No long-term sick leaves (> 1 month) or turnover | 19,674 (98.4) | 10,841 (98.5) | 8,833 (98.3) | ||
| Long-term sick leave | 136 (0.7) | 77 (0.7) | 59 (0.7) | ||
| Turnover | 171 (0.9) | 84 (0.8) | 87 (1.0) | ||
| Both long-term sick leave and turnover | 19 (0.1) | 9 (0.1) | 10 (0.1) | ||
| Antidepressant use, n (%) | 1,016 (5.1) | 579 (5.3) | 437 (4.9) | 0.204 | |
| Presenteeism (HPQ score ≤40), n (%) | 2,051 (10.3) | 1,260 (11.4) | 791 (8.8) | <0.001 | |
SD: standard deviation; HPQ: World Health Organization Health and Work Performance Questionnaire.
The data on mental health outcomes are shown in Table 2. For sick leave data, 136 workers reported long-term sick leave (over one month), 171 workers reported turnover, and 19 workers reported both long-term sick leave and turnover due to mental health problems in the previous year. Workers with presenteeism were more prevalent among male participants (χ2(1)=24.0, p<0.001), while workers with poor mental health, as assessed using the K6 scale, were more prevalent among female participants (χ2(1)=37.6, p<0.001).
Of the 4,738 workers excluded from the analysis, 2,151 exhibited inappropriate responses to the instructional manipulation check, 2,187 exhibited extremely short response times, and 400 exhibited both inappropriate and extremely short response times. When comparing the final and excluded samples (Supplementary Tables 1 and 2), we found some differences in demographic and occupational characteristics as well as mental health outcomes between the two groups. For example, among the excluded sample, male workers (χ2(1)=5.8, p=0.016), workers with regular employment (χ2(1)=5.2, p=0.023), workers with telework (χ2(1)=15.8, p<0.001), and workers with poor mental health (χ2(1)=5.6, p=0.018) were more prevalent.
Discussion
The purpose of this web-based large-scale cohort study was to investigate the associations between self-care behaviors in daily life, work environment, and mental health in a representative Japanese employee sample. We sampled participants according to the distribution ratios of Japanese employees by industry, age group, and sex. This sampling allowed us to investigate the differences in the effects of self-care behaviors and work environment improvements on mental health for participants from different occupations and working styles (e.g., physical work, night work, and telework).
When conducting surveys on internet monitors, insincere responses of participants could be a concern because a portion of the monitors were likely to participate in the survey to only receive an incentive in the form of points. Considering this problem, we created a question as an instructional manipulation check to exclude insincere responses. Previously, excluding the insincere respondents by this item improved the response quality of the web survey (e.g. fewer “don’t know” options, fewer straight line responses)17). In addition, excluding participants with short response times has been commonly used to detect fraudulent responses in previous studies22). However, the exclusion of participants with insincere responses could have caused a selection bias. We found significant differences in demographic and occupational backgrounds as well as mental health outcomes between the final sample and excluded participants (Supplementary Tables 1 and 2), which need to be carefully considered in future studies.
In addition, the survey itself, which targeted internet monitors, may have induced selection bias. However, the demographic data presented in Tables 1 and 2 are comparable to those reported in previous studies. For example, the percentages of male (30.0%) and female (13.0%) smokers were comparable to those reported in previous studies (36.5% and 13.4%, respectively)23). Regarding mental health outcomes, in previous studies on Japanese employees12, 24, 25), the average K6 scores varied across studies, with variations ranging from 3.0–5.4. The average score in this study (4.8) was within the range reported in previous studies. Furthermore, for example, workers with service occupations, irregular employment, shorter work hours, and lower incomes were more prevalent among female participants than among male workers, which is similar to the characteristics of Japanese female workers previously reported26). Therefore, an overview of the baseline survey data suggests that representative Japanese employees can be sampled without a serious selection bias.
This study has some limitations. Firstly, the baseline survey was conducted during the COVID-19 pandemic. The pandemic has significantly altered daily lifestyle, work environment, and mental health. We asked questions about COVID-19 (e.g., previous infection with COVID-19) as well as the frequency of teleworking and the amount of social interactions. These factors should be considered when analyzing data in future studies, as the K6 score, an index of mental health, was within the range reported in previous studies, as discussed above. Secondly, due to the nature of the web-based survey, the mental health outcomes of this study were assessed in a self-reported manner but not objectively. Furthermore, this study would demonstrate prospective associations but not causal associations between self-care behaviors, work environment, and mental health problems because we did not conduct an intervention study.
In conclusion, this study described the design of WELWEL and the demographic data of the participants in the baseline survey. We plan to use these survey data in the future to examine the associations between daily lifestyles, work environments, and the mental health of Japanese employees.
Conflict of Interest
The authors have no conflicts of interest to declare regarding this study.
Supplementary Material
Acknowledgments
This study was supported by a research grant from the National Institute of Occupational Safety and Health of Japan (N-P03-02).
References
- 1.Ministry of Health, Labour and Welfare. Survey on industrial safety and health (actual condition survey) https://www.mhlw.go.jp/toukei/list/r03-46–50.html. Accessed April 24, 2023.
- 2.Kawakami N, Tsutsumi A. (2016) The Stress Check Program: a new national policy for monitoring and screening psychosocial stress in the workplace in Japan. J Occup Health 58, 1–6. [DOI] [PubMed] [Google Scholar]
- 3.Tsutsumi A, Nagami M, Yoshikawa T, Kogi K, Kawakami N. (2009) Participatory intervention for workplace improvements on mental health and job performance among blue-collar workers: a cluster randomized controlled trial. J Occup Environ Med 51, 554–63. [DOI] [PubMed] [Google Scholar]
- 4.Uchiyama A, Odagiri Y, Ohya Y, Takamiya T, Inoue S, Shimomitsu T. (2013) Effect on mental health of a participatory intervention to improve psychosocial work environment: a cluster randomized controlled trial among nurses. J Occup Health 55, 173–83. [DOI] [PubMed] [Google Scholar]
- 5.Kawakami N, Haratani T, Iwata N, Imanaka Y, Murata K, Araki S. (1999) Effects of mailed advice on stress reduction among employees in Japan: a randomized controlled trial. Ind Health 37, 237–42. [DOI] [PubMed] [Google Scholar]
- 6.Ikeda H, Kubo T, Izawa S, Nakamura-Taira N, Yoshikawa T, Akamatsu R. (2022) The joint association of daily rest periods and sleep duration with worker health and productivity: a cross-sectional web survey of Japanese daytime workers. Int J Environ Res Public Health 19, 11143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ministry of Internal Affairs and Communications. Population census. https://www.stat.go.jp/data/kokusei/2015/kekka.html. Accessed April 24, 2023.
- 8.Kessler RC, Andrews G, Colpe LJ, Hiripi E, Mroczek DK, Normand SL, Walters EE, Zaslavsky AM. (2002) Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med 32, 959–76. [DOI] [PubMed] [Google Scholar]
- 9.Furukawa TA, Kawakami N, Saitoh M, Ono Y, Nakane Y, Nakamura Y, Tachimori H, Iwata N, Uda H, Nakane H, Watanabe M, Naganuma Y, Hata Y, Kobayashi M, Miyake Y, Takeshima T, Kikkawa T. (2008) The performance of the Japanese version of the K6 and K10 in the World Mental Health Survey Japan. Int J Methods Psychiatr Res 17, 152–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, Howes MJ, Normand SL, Manderscheid RW, Walters EE, Zaslavsky AM. (2003) Screening for serious mental illness in the general population. Arch Gen Psychiatry 60, 184–9. [DOI] [PubMed] [Google Scholar]
- 11.Kawakami N, Inoue A, Tsuchiya M, Watanabe K, Imamura K, Iida M, Nishi D. (2020) Construct validity and test-retest reliability of the World Mental Health Japan version of the World Health Organization Health and Work Performance Questionnaire Short Version: a preliminary study. Ind Health 58, 375–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Suzuki T, Miyaki K, Sasaki Y, Song Y, Tsutsumi A, Kawakami N, Shimazu A, Takahashi M, Inoue A, Kurioka S, Shimbo T. (2014) Optimal cutoff values of WHO-HPQ presenteeism scores by ROC analysis for preventing mental sickness absence in Japanese prospective cohort. PLoS One 9, e111191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Japan Institute for Labour Policy and Training. Mentaruherusu, sishoubyou nadono chiryou to shokugyouseikatu no ryouritusien ni kansuru chousa. https://www.jil.go.jp/institute/research/2013/112.html. Accessed April 24, 2023.
- 14.Yoshikawa T, Kawakami N, Kogi K, Tsutsumi A, Shimazu M, Nagami M, Shimazu A. (2007) [Development of a mental health action checklist for improving workplace environment as means of job stress prevention]. Sangyo Eiseigaku Zasshi 49, 127–42(in Japanese). [DOI] [PubMed] [Google Scholar]
- 15.Shimomitsu T, Haratani T, Nakamura K, Kawakami N, Hayashi T, Hiro H, Arai M, Miyazaki S, Furuki K, Ohya Y, Odagiri Y .(2000) Final development of the Brief Job Stress Questionnaire mainly used for assessment of the individuals. In: The Ministry of Labor sponsored grant for the prevention of work-related illness, FY 1999 report, Kato M (Ed.), 126–64, Tokyo Medical University, Tokyo (in Japanese). [Google Scholar]
- 16.Inoue A, Kawakami N, Shimomitsu T, Tsutsumi A, Haratani T, Yoshikawa T, Shimazu A, Odagiri Y. (2014) Development of a short questionnaire to measure an extended set of job demands, job resources, and positive health outcomes: the new brief job stress questionnaire. Ind Health 52, 175–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Masuda S, Sakagami T, Morii M. (2019) Comparison among methods for improving response quality of surveys. Shinrigaku Kenkyu 90, 463–72. [Google Scholar]
- 18.Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49, 1373–9. [DOI] [PubMed] [Google Scholar]
- 19.Tsuchiya M, Kawakami N, Ono Y, Nakane Y, Nakamura Y, Fukao A, Tachimori H, Iwata N, Uda H, Nakane H, Watanabe M, Oorui M, Naganuma Y, Furukawa TA, Kobayashi M, Ahiko T, Takeshima T, Kikkawa T. (2012) Impact of mental disorders on work performance in a community sample of workers in Japan: the World Mental Health Japan Survey 2002–2005. Psychiatry Res 198, 140–5. [DOI] [PubMed] [Google Scholar]
- 20.Izawa S, Nakamura-Taira N, Yamada KC. (2016) Stress underestimation and mental health outcomes in male Japanese workers: a 1-year prospective study. Int J Behav Med 23, 664–9. [DOI] [PubMed] [Google Scholar]
- 21.Ikeda H, Kubo T, Sasaki T, Nishimura Y, Liu X, Matsuo T, So R, Matsumoto S, Takahashi M. (2022) Prospective changes in sleep problems in response to the daily rest period among Japanese daytime workers: a longitudinal web survey. J Sleep Res 31, e13449. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Eguchi H, Hino A, Inoue A, Tsuji M, Tateishi S, Ando H, Nagata T, Matsuda S, Fujino Y. (2021) Effect of anxiety about COVID-19 infection in the workplace on the association between job demands and psychological distress. Front Public Health 9, 722071. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Takada M, Kondo N, Hashimoto H, J-SHINE Data Management Committee (2014) Japanese study on stratification, health, income, and neighborhood: study protocol and profiles of participants. J Epidemiol 24, 334–44. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Inoue A, Kawakami N, Tsuchiya M, Sakurai K, Hashimoto H. (2010) Association of occupation, employment contract, and company size with mental health in a national representative sample of employees in Japan. J Occup Health 52, 227–40. [DOI] [PubMed] [Google Scholar]
- 25.Eguchi H, Tsutsumi A, Inoue A, Hikichi H, Kawachi I. (2018) Association of workplace social capital with psychological distress: results from a longitudinal multilevel analysis of the J-HOPE Study. BMJ Open 8, e022569. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Ariyoshi M, Nishikitani M, Tsurugano S, Inoue M, Yano E. (2021) The association of health status and employment status for each occupation: results from a comprehensive survey of living conditions in Japan. Jpn Psychol Res 65, 201–14. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.